|
Title
|
|
|
|
Feasibility of Kd-trees in Gaussian process regression to partition test points in high resolution input space
| |
|
Author
|
|
|
|
| |
|
Abstract
|
|
|
|
| Bayesian inference using Gaussian processes on large datasets have been studied extensively over the past few years. However, little attention has been given on how to apply these on a high resolution input space. By approximating the set of test points (where we want to make predictions, not the set of training points in the dataset) by a kd-tree, a multi-resolution data structure arises that allows for considerable gains in performance and memory usage without a significant loss of accuracy. In this paper, we study the feasibility and efficiency of constructing and using such a kd-tree in Gaussian process regression. We propose a cut-off rule that is easy to interpret and to tune. We show our findings on generated toy data in a 3D point cloud and a simulated 2D vibrometry example. This survey is beneficial for researchers that are working on a high resolution input space. The kd-tree approximation outperforms the naïve Gaussian process implementation in all experiments. |
| |
|
Language
|
|
|
|
English
| |
|
Source (journal)
|
|
|
|
Algorithms
| |
|
Publication
|
|
|
|
2020
| |
|
ISSN
|
|
|
|
1999-4893
| |
|
DOI
|
|
|
|
10.3390/A13120327
| |
|
Volume/pages
|
|
|
|
13
:12
(2020)
, 14 p.
| |
|
Article Reference
|
|
|
|
327
| |
|
ISI
|
|
|
|
000601846600001
| |
|
Medium
|
|
|
|
E-only publicatie
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|
Full text (open access)
|
|
|
|
| |
|